Recent advances in search, machine learning, and natural-language processing have made it possible to extract structured information from free text, providing a new and largely untapped source of insight for well and reservoir planning. However, major challenges are involved in applying these techniques to data that are messy or that lack a labeled training set. This paper presents a method to compare the distribution of hypothesized and realized risks to oil wells described in two data sets that contain free-text descriptions of risks.
In the oil and gas industry, risk identification and risk assessment are critical. This holds particularly true during the drilling stages, which cannot begin before a risk assessment is conducted. While these risk assessments are typically conducted in a group setting, the project drilling engineer usually has a predetermined list of risks and likelihood scores that are the focus of the conversation.
One problem with this approach is that drilling engineers are inherently biased by personal experiences, which can affect their view on how likely an event is to happen. For example, if a project drilling engineer recently encountered well-control issues, the engineer will likely overestimate the chance of future well-control issues. On the other hand, if the engineer has never encountered a well-control issue, it may be unintentionally omitted altogether from the risk assessments....
Natural-Language-Processing Techniques for Oil and Gas Drilling Data
01 October 2017